Overview

Dataset statistics

Number of variables11
Number of observations19020
Missing cells0
Missing cells (%)0.0%
Duplicate rows115
Duplicate rows (%)0.6%
Total size in memory1.6 MiB
Average record size in memory88.0 B

Variable types

NUM10
BOOL1

Reproduction

Analysis started2020-08-25 01:30:54.930787
Analysis finished2020-08-25 01:31:12.254974
Duration17.32 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Dataset has 115 (0.6%) duplicate rows Duplicates
FConc1 is highly correlated with FConcHigh correlation
FConc is highly correlated with FConc1High correlation

Variables

FLength
Real number (ℝ≥0)

Distinct count18643
Unique (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.25015392744479
Minimum4.2835
Maximum334.17699999999996
Zeros0
Zeros (%)0.0%
Memory size148.7 KiB
2020-08-25T01:31:12.311757image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum4.2835
5-th percentile16.433655
Q124.336
median37.1477
Q370.122175
95-th percentile139.72515
Maximum334.177
Range329.8935
Interquartile range (IQR)45.786175

Descriptive statistics

Standard deviation42.36485494
Coefficient of variation (CV)0.7955818306
Kurtosis4.970441241
Mean53.25015393
Median Absolute Deviation (MAD)16.32565
Skewness2.013652324
Sum1012817.928
Variance1794.780934
2020-08-25T01:31:12.427719image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
24.83323< 0.1%
 
12.91763< 0.1%
 
20.75223< 0.1%
 
26.91873< 0.1%
 
19.15723< 0.1%
 
13.11792< 0.1%
 
21.54522< 0.1%
 
21.38062< 0.1%
 
19.34692< 0.1%
 
24.46992< 0.1%
 
35.70622< 0.1%
 
22.26962< 0.1%
 
42.38862< 0.1%
 
97.86822< 0.1%
 
19.11392< 0.1%
 
16.25182< 0.1%
 
24.9992< 0.1%
 
21.3082< 0.1%
 
37.73612< 0.1%
 
13.02872< 0.1%
 
20.28912< 0.1%
 
68.41312< 0.1%
 
12.98232< 0.1%
 
51.37892< 0.1%
 
99.93642< 0.1%
 
Other values (18618)1896599.7%
 
ValueCountFrequency (%) 
4.28351< 0.1%
 
7.20791< 0.1%
 
7.36061< 0.1%
 
8.05181< 0.1%
 
8.23041< 0.1%
 
8.23111< 0.1%
 
8.48021< 0.1%
 
8.57381< 0.1%
 
8.6011< 0.1%
 
8.69981< 0.1%
 
ValueCountFrequency (%) 
334.1771< 0.1%
 
310.611< 0.1%
 
305.4221< 0.1%
 
305.3241< 0.1%
 
305.09611< 0.1%
 
303.56761< 0.1%
 
303.27871< 0.1%
 
299.93041< 0.1%
 
297.12391< 0.1%
 
295.6721< 0.1%
 

FWidth
Real number (ℝ≥0)

Distinct count18200
Unique (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.180966219768667
Minimum0.0
Maximum256.382
Zeros98
Zeros (%)0.5%
Memory size148.7 KiB
2020-08-25T01:31:12.759142image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.4005
Q111.8638
median17.1399
Q324.739475
95-th percentile58.479245
Maximum256.382
Range256.382
Interquartile range (IQR)12.875675

Descriptive statistics

Standard deviation18.3460563
Coefficient of variation (CV)0.8271080761
Kurtosis16.76540668
Mean22.18096622
Median Absolute Deviation (MAD)5.87145
Skewness3.371627981
Sum421881.9775
Variance336.5777816
2020-08-25T01:31:12.864181image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0980.5%
 
10.75394< 0.1%
 
17.68523< 0.1%
 
11.95173< 0.1%
 
17.28143< 0.1%
 
20.20213< 0.1%
 
10.03423< 0.1%
 
18.75243< 0.1%
 
15.86443< 0.1%
 
13.73463< 0.1%
 
15.02953< 0.1%
 
0.00293< 0.1%
 
9.55133< 0.1%
 
10.50843< 0.1%
 
12.81553< 0.1%
 
0.00333< 0.1%
 
18.82473< 0.1%
 
0.00013< 0.1%
 
12.72713< 0.1%
 
10.58323< 0.1%
 
7.29823< 0.1%
 
0.00283< 0.1%
 
6.65512< 0.1%
 
12.69792< 0.1%
 
16.86532< 0.1%
 
Other values (18175)1885299.1%
 
ValueCountFrequency (%) 
0980.5%
 
0.00013< 0.1%
 
0.00021< 0.1%
 
0.00061< 0.1%
 
0.00191< 0.1%
 
0.00252< 0.1%
 
0.00262< 0.1%
 
0.00271< 0.1%
 
0.00283< 0.1%
 
0.00293< 0.1%
 
ValueCountFrequency (%) 
256.3821< 0.1%
 
228.03851< 0.1%
 
220.51441< 0.1%
 
201.3641< 0.1%
 
190.54321< 0.1%
 
190.1391< 0.1%
 
188.88661< 0.1%
 
186.9281< 0.1%
 
179.29241< 0.1%
 
177.7821< 0.1%
 

FSize
Real number (ℝ≥0)

Distinct count7228
Unique (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.825016961093586
Minimum1.9413
Maximum5.3233
Zeros0
Zeros (%)0.0%
Memory size148.7 KiB
2020-08-25T01:31:12.980982image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.9413
5-th percentile2.1945
Q12.4771
median2.7396
Q33.1016
95-th percentile3.71575
Maximum5.3233
Range3.382
Interquartile range (IQR)0.6245

Descriptive statistics

Standard deviation0.4725986487
Coefficient of variation (CV)0.1672905527
Kurtosis0.7272784359
Mean2.825016961
Median Absolute Deviation (MAD)0.29895
Skewness0.8755071709
Sum53731.8226
Variance0.2233494827
2020-08-25T01:31:13.104441image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2.1508270.1%
 
2.0774240.1%
 
2.1287240.1%
 
2.1319230.1%
 
2.1351220.1%
 
2.1414220.1%
 
2.3139220.1%
 
2.3936210.1%
 
2.29210.1%
 
2.5315200.1%
 
2.3483200.1%
 
2.3589200.1%
 
2.1717200.1%
 
2.2504190.1%
 
2.2190.1%
 
2.4914190.1%
 
2.3454190.1%
 
2.4526190.1%
 
2.4158190.1%
 
2.0881190.1%
 
2.1189190.1%
 
2.3531180.1%
 
2.1538180.1%
 
2.2625180.1%
 
2.1658180.1%
 
Other values (7203)1851097.3%
 
ValueCountFrequency (%) 
1.94131< 0.1%
 
1.94681< 0.1%
 
1.99161< 0.1%
 
1.99781< 0.1%
 
2.00221< 0.1%
 
2.00652< 0.1%
 
2.01073< 0.1%
 
2.01494< 0.1%
 
2.01911< 0.1%
 
2.02338< 0.1%
 
ValueCountFrequency (%) 
5.32331< 0.1%
 
5.17951< 0.1%
 
5.14671< 0.1%
 
5.01181< 0.1%
 
5.011< 0.1%
 
4.99461< 0.1%
 
4.95181< 0.1%
 
4.93691< 0.1%
 
4.9051< 0.1%
 
4.85011< 0.1%
 

FConc
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count6410
Unique (%)33.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3803270715036804
Minimum0.0131
Maximum0.893
Zeros0
Zeros (%)0.0%
Memory size148.7 KiB
2020-08-25T01:31:13.223760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.0131
5-th percentile0.1263
Q10.2358
median0.35415
Q30.5037
95-th percentile0.734205
Maximum0.893
Range0.8799
Interquartile range (IQR)0.2679

Descriptive statistics

Standard deviation0.1828131472
Coefficient of variation (CV)0.4806735069
Kurtosis-0.5212970988
Mean0.3803270715
Median Absolute Deviation (MAD)0.13025
Skewness0.4858884539
Sum7233.8209
Variance0.0334206468
2020-08-25T01:31:13.341773image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.6160.1%
 
0.4116120.1%
 
0.4120.1%
 
0.2979120.1%
 
0.2214110.1%
 
0.193110.1%
 
0.2175110.1%
 
0.2408110.1%
 
0.5110.1%
 
0.6154110.1%
 
0.1777100.1%
 
0.2562100.1%
 
0.1951100.1%
 
0.3846100.1%
 
0.5714100.1%
 
0.3108100.1%
 
0.2598100.1%
 
0.4709100.1%
 
0.2313100.1%
 
0.1876100.1%
 
0.5012100.1%
 
0.3979100.1%
 
0.2689100.1%
 
0.2857100.1%
 
0.1854100.1%
 
Other values (6385)1875298.6%
 
ValueCountFrequency (%) 
0.01311< 0.1%
 
0.01331< 0.1%
 
0.01371< 0.1%
 
0.01392< 0.1%
 
0.01581< 0.1%
 
0.01621< 0.1%
 
0.01711< 0.1%
 
0.01881< 0.1%
 
0.01961< 0.1%
 
0.02061< 0.1%
 
ValueCountFrequency (%) 
0.8931< 0.1%
 
0.89121< 0.1%
 
0.88891< 0.1%
 
0.88461< 0.1%
 
0.87861< 0.1%
 
0.87781< 0.1%
 
0.87721< 0.1%
 
0.87571< 0.1%
 
0.87451< 0.1%
 
0.87431< 0.1%
 

FConc1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count4421
Unique (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21465713459516297
Minimum0.0003
Maximum0.6752
Zeros0
Zeros (%)0.0%
Memory size148.7 KiB
2020-08-25T01:31:13.478223image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.0003
5-th percentile0.066995
Q10.128475
median0.1965
Q30.285225
95-th percentile0.42241
Maximum0.6752
Range0.6749
Interquartile range (IQR)0.15675

Descriptive statistics

Standard deviation0.1105107989
Coefficient of variation (CV)0.5148247185
Kurtosis0.0293910244
Mean0.2146571346
Median Absolute Deviation (MAD)0.0754
Skewness0.6856946259
Sum4082.7787
Variance0.01221263667
2020-08-25T01:31:13.589000image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.194180.1%
 
0.2160.1%
 
0.2126160.1%
 
0.1939160.1%
 
0.217150.1%
 
0.2251150.1%
 
0.1581140.1%
 
0.1568140.1%
 
0.1515140.1%
 
0.1458140.1%
 
0.1279140.1%
 
0.169140.1%
 
0.1504140.1%
 
0.0934140.1%
 
0.1772140.1%
 
0.1208140.1%
 
0.1812140.1%
 
0.1245140.1%
 
0.1665140.1%
 
0.1302140.1%
 
0.1247140.1%
 
0.1404130.1%
 
0.1121130.1%
 
0.1922130.1%
 
0.1429130.1%
 
Other values (4396)1866298.1%
 
ValueCountFrequency (%) 
0.00031< 0.1%
 
0.00081< 0.1%
 
0.00111< 0.1%
 
0.00151< 0.1%
 
0.0021< 0.1%
 
0.00471< 0.1%
 
0.0051< 0.1%
 
0.00721< 0.1%
 
0.00731< 0.1%
 
0.00761< 0.1%
 
ValueCountFrequency (%) 
0.67521< 0.1%
 
0.6741< 0.1%
 
0.6431< 0.1%
 
0.6371< 0.1%
 
0.62961< 0.1%
 
0.62831< 0.1%
 
0.62641< 0.1%
 
0.62421< 0.1%
 
0.62241< 0.1%
 
0.62041< 0.1%
 

FAsym
Real number (ℝ)

Distinct count18704
Unique (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.331745157728706
Minimum-457.9161
Maximum575.2407
Zeros41
Zeros (%)0.2%
Memory size148.7 KiB
2020-08-25T01:31:13.717717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-457.9161
5-th percentile-111.1947
Q1-20.58655
median4.01305
Q324.0637
95-th percentile65.544125
Maximum575.2407
Range1033.1568
Interquartile range (IQR)44.65025

Descriptive statistics

Standard deviation59.20606198
Coefficient of variation (CV)-13.66794671
Kurtosis8.155329763
Mean-4.331745158
Median Absolute Deviation (MAD)21.68065
Skewness-1.046441472
Sum-82389.7929
Variance3505.357776
2020-08-25T01:31:13.824778image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0410.2%
 
-0.00017< 0.1%
 
-1.47613< 0.1%
 
-0.50623< 0.1%
 
8.80773< 0.1%
 
7.10883< 0.1%
 
-79.10032< 0.1%
 
-87.18812< 0.1%
 
-14.09852< 0.1%
 
-4.60422< 0.1%
 
-19.12022< 0.1%
 
2.03752< 0.1%
 
-18.37062< 0.1%
 
-69.49122< 0.1%
 
-204.7672< 0.1%
 
20.71482< 0.1%
 
-27.95522< 0.1%
 
-1.22942< 0.1%
 
-0.74112< 0.1%
 
22.23372< 0.1%
 
-129.0542< 0.1%
 
12.23342< 0.1%
 
143.632< 0.1%
 
28.41072< 0.1%
 
-1.35832< 0.1%
 
Other values (18679)1892299.5%
 
ValueCountFrequency (%) 
-457.91611< 0.1%
 
-449.95261< 0.1%
 
-382.5941< 0.1%
 
-381.7341< 0.1%
 
-378.94571< 0.1%
 
-368.6331< 0.1%
 
-363.33821< 0.1%
 
-353.9341< 0.1%
 
-353.261< 0.1%
 
-349.7571< 0.1%
 
ValueCountFrequency (%) 
575.24071< 0.1%
 
473.06541< 0.1%
 
464.6311< 0.1%
 
444.4011< 0.1%
 
433.09571< 0.1%
 
402.9251< 0.1%
 
402.18631< 0.1%
 
400.2841< 0.1%
 
396.33791< 0.1%
 
384.34771< 0.1%
 

FM3Long
Real number (ℝ)

Distinct count18693
Unique (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.545544815983174
Minimum-331.78
Maximum238.321
Zeros39
Zeros (%)0.2%
Memory size148.7 KiB
2020-08-25T01:31:13.954296image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-331.78
5-th percentile-80.28369
Q1-12.842775
median15.3141
Q335.8378
95-th percentile83.07177
Maximum238.321
Range570.101
Interquartile range (IQR)48.680575

Descriptive statistics

Standard deviation51.00011801
Coefficient of variation (CV)4.836176689
Kurtosis4.670973798
Mean10.54554482
Median Absolute Deviation (MAD)25.33365
Skewness-1.123078055
Sum200576.2624
Variance2601.012037
2020-08-25T01:31:14.058861image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0390.2%
 
-0.00014< 0.1%
 
16.07473< 0.1%
 
-15.72212< 0.1%
 
10.59272< 0.1%
 
-16.57972< 0.1%
 
51.29832< 0.1%
 
-127.2562< 0.1%
 
-162.3272< 0.1%
 
20.1332< 0.1%
 
62.56762< 0.1%
 
22.53452< 0.1%
 
-188.0322< 0.1%
 
14.57022< 0.1%
 
89.51952< 0.1%
 
18.59612< 0.1%
 
11.11482< 0.1%
 
7.6522< 0.1%
 
-16.5172< 0.1%
 
-6.48472< 0.1%
 
10.01382< 0.1%
 
37.33362< 0.1%
 
11.04872< 0.1%
 
40.66392< 0.1%
 
11.8312< 0.1%
 
Other values (18668)1893099.5%
 
ValueCountFrequency (%) 
-331.781< 0.1%
 
-318.30021< 0.1%
 
-297.17171< 0.1%
 
-293.17621< 0.1%
 
-287.50671< 0.1%
 
-287.36361< 0.1%
 
-284.70381< 0.1%
 
-281.95411< 0.1%
 
-281.8441< 0.1%
 
-281.4351< 0.1%
 
ValueCountFrequency (%) 
238.3211< 0.1%
 
231.4461< 0.1%
 
227.81741< 0.1%
 
226.35061< 0.1%
 
222.4171< 0.1%
 
217.9341< 0.1%
 
217.6241< 0.1%
 
216.9851< 0.1%
 
215.8941< 0.1%
 
203.8631< 0.1%
 

FM3Trans
Real number (ℝ)

Distinct count18390
Unique (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2497259568874868
Minimum-205.8947
Maximum179.851
Zeros59
Zeros (%)0.3%
Memory size148.7 KiB
2020-08-25T01:31:14.177643image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-205.8947
5-th percentile-25.76384
Q1-10.849375
median0.6662
Q310.946425
95-th percentile26.99851
Maximum179.851
Range385.7457
Interquartile range (IQR)21.7958

Descriptive statistics

Standard deviation20.82743895
Coefficient of variation (CV)83.40117786
Kurtosis8.580352473
Mean0.2497259569
Median Absolute Deviation (MAD)10.888
Skewness0.1201212735
Sum4749.7877
Variance433.7822131
2020-08-25T01:31:14.286413image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0590.3%
 
-0.0001240.1%
 
0.0001180.1%
 
9.52313< 0.1%
 
-5.44543< 0.1%
 
11.16023< 0.1%
 
-8.9753< 0.1%
 
-7.66013< 0.1%
 
6.18293< 0.1%
 
-6.23763< 0.1%
 
10.90153< 0.1%
 
-11.66042< 0.1%
 
18.51932< 0.1%
 
8.65132< 0.1%
 
8.81482< 0.1%
 
18.79242< 0.1%
 
-9.6452< 0.1%
 
9.06082< 0.1%
 
4.81382< 0.1%
 
-5.55272< 0.1%
 
12.972< 0.1%
 
-12.35092< 0.1%
 
-10.64312< 0.1%
 
-6.51932< 0.1%
 
8.46552< 0.1%
 
Other values (18365)1886799.2%
 
ValueCountFrequency (%) 
-205.89471< 0.1%
 
-164.141< 0.1%
 
-149.55131< 0.1%
 
-142.58941< 0.1%
 
-142.1191< 0.1%
 
-135.50511< 0.1%
 
-134.751< 0.1%
 
-134.3951< 0.1%
 
-133.13591< 0.1%
 
-132.4161< 0.1%
 
ValueCountFrequency (%) 
179.8511< 0.1%
 
170.6921< 0.1%
 
163.26971< 0.1%
 
154.8651< 0.1%
 
143.87531< 0.1%
 
139.23611< 0.1%
 
132.5891< 0.1%
 
132.3881< 0.1%
 
131.55471< 0.1%
 
130.85451< 0.1%
 

FAlpha
Real number (ℝ≥0)

Distinct count17981
Unique (%)94.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.645706677181913
Minimum0.0
Maximum90.0
Zeros5
Zeros (%)< 0.1%
Memory size148.7 KiB
2020-08-25T01:31:14.414155image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.933285
Q15.547925
median17.6795
Q345.88355
95-th percentile80.72654
Maximum90
Range90
Interquartile range (IQR)40.335625

Descriptive statistics

Standard deviation26.10362051
Coefficient of variation (CV)0.9442196872
Kurtosis-0.5337036036
Mean27.64570668
Median Absolute Deviation (MAD)14.6924
Skewness0.8508898774
Sum525821.341
Variance681.3990037
2020-08-25T01:31:14.525815image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.00027< 0.1%
 
05< 0.1%
 
3.41614< 0.1%
 
0.8044< 0.1%
 
904< 0.1%
 
1.294< 0.1%
 
2.7014< 0.1%
 
2.764< 0.1%
 
0.2564< 0.1%
 
0.3864< 0.1%
 
6.813< 0.1%
 
1.7013< 0.1%
 
4.423< 0.1%
 
1.1213< 0.1%
 
25.8313< 0.1%
 
26.9023< 0.1%
 
1.5343< 0.1%
 
1.6763< 0.1%
 
10.8363< 0.1%
 
2.6223< 0.1%
 
3.6133< 0.1%
 
4.1993< 0.1%
 
7.1163< 0.1%
 
0.5833< 0.1%
 
0.0353< 0.1%
 
Other values (17956)1893199.5%
 
ValueCountFrequency (%) 
05< 0.1%
 
0.00027< 0.1%
 
0.00032< 0.1%
 
0.0011< 0.1%
 
0.00311< 0.1%
 
0.00561< 0.1%
 
0.00861< 0.1%
 
0.0091< 0.1%
 
0.00971< 0.1%
 
0.01031< 0.1%
 
ValueCountFrequency (%) 
904< 0.1%
 
89.97981< 0.1%
 
89.95791< 0.1%
 
89.95351< 0.1%
 
89.95281< 0.1%
 
89.92291< 0.1%
 
89.91551< 0.1%
 
89.90871< 0.1%
 
89.90761< 0.1%
 
89.90421< 0.1%
 

FDist
Real number (ℝ≥0)

Distinct count18437
Unique (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean193.81802646687697
Minimum1.2826
Maximum495.561
Zeros0
Zeros (%)0.0%
Memory size148.7 KiB
2020-08-25T01:31:14.652573image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.2826
5-th percentile71.41369
Q1142.49225
median191.85145
Q3240.563825
95-th percentile326.659975
Maximum495.561
Range494.2784
Interquartile range (IQR)98.071575

Descriptive statistics

Standard deviation74.73178696
Coefficient of variation (CV)0.3855770711
Kurtosis-0.112576594
Mean193.8180265
Median Absolute Deviation (MAD)49.0165
Skewness0.2295873764
Sum3686418.863
Variance5584.839983
2020-08-25T01:31:14.758865image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
188.8673< 0.1%
 
159.843< 0.1%
 
209.9543< 0.1%
 
265.2383< 0.1%
 
182.0133< 0.1%
 
100.3953< 0.1%
 
246.0133< 0.1%
 
168.7743< 0.1%
 
227.1073< 0.1%
 
229.2963< 0.1%
 
187.6513< 0.1%
 
148.3723< 0.1%
 
216.0323< 0.1%
 
185.9273< 0.1%
 
186.8283< 0.1%
 
295.343< 0.1%
 
185.9093< 0.1%
 
116.7373< 0.1%
 
195.2873< 0.1%
 
146.3543< 0.1%
 
198.5312< 0.1%
 
182.5582< 0.1%
 
139.6442< 0.1%
 
211.032< 0.1%
 
151.4272< 0.1%
 
Other values (18412)1895099.6%
 
ValueCountFrequency (%) 
1.28261< 0.1%
 
5.54491< 0.1%
 
5.59221< 0.1%
 
5.69981< 0.1%
 
5.74561< 0.1%
 
6.5641< 0.1%
 
6.68521< 0.1%
 
9.15741< 0.1%
 
13.11081< 0.1%
 
14.02291< 0.1%
 
ValueCountFrequency (%) 
495.5611< 0.1%
 
466.40781< 0.1%
 
450.9531< 0.1%
 
450.4021< 0.1%
 
450.3491< 0.1%
 
448.02951< 0.1%
 
446.4881< 0.1%
 
438.9011< 0.1%
 
438.85741< 0.1%
 
437.4771< 0.1%
 

target
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size148.7 KiB
0
12332
1
6688
ValueCountFrequency (%) 
01233264.8%
 
1668835.2%
 

Interactions

2020-08-25T01:30:55.976095image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:30:56.156300image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:30:56.355912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:30:56.548298image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:30:56.696731image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:30:56.858322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:30:57.026402image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:30:57.179557image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:30:57.346155image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:30:57.492956image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:30:57.642231image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:30:57.788119image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:30:58.123584image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:30:58.279437image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:30:58.421934image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:30:58.585023image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:30:58.741280image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:30:58.884974image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:30:59.033827image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:30:59.171223image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:30:59.317996image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:30:59.478838image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:30:59.634661image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:30:59.789580image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:30:59.935425image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:00.089929image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:00.240637image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:00.392971image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:00.552792image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:00.696126image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:00.843376image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:00.990540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:01.140396image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:01.282144image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:01.418078image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:01.575526image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:01.723028image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:01.861163image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:02.007557image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:02.143013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:02.280471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:02.442974image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:02.596843image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:02.755641image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:03.115053image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:03.280262image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:03.436023image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:03.593727image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:03.763730image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:03.920854image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:04.088938image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:04.236688image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:04.377324image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:04.524567image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:04.663681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:04.812035image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:04.955457image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:05.095672image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:05.248018image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:05.387792image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:05.531255image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:05.680305image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:05.826164image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:05.974840image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:06.113425image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:06.265137image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:06.407842image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:06.550348image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:06.709260image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:06.860086image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:07.006633image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:07.166948image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:07.318752image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:07.479013image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:07.625121image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:07.992516image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:08.144069image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:08.298202image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:08.459048image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:08.610994image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:08.763123image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:08.914356image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:09.056189image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:09.203229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:09.336183image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:09.483819image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:09.617973image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:09.753633image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:09.898149image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:10.033959image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:10.174279image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:10.335673image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:10.485396image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:10.638612image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:10.792338image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:10.944889image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:11.090463image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:11.234396image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:11.388208image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:11.530468image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T01:31:14.885391image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T01:31:15.126028image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T01:31:15.358181image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T01:31:15.596892image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T01:31:11.795882image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:31:12.099139image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

FLengthFWidthFSizeFConcFConc1FAsymFM3LongFM3TransFAlphaFDisttarget
028.796716.00212.64490.39180.198227.700422.0110-8.202740.092081.88280
131.603611.72352.51850.53030.377326.272223.8238-9.95746.3609205.26100
2162.0520136.03104.06120.03740.0187116.7410-64.8580-45.216076.9600256.78800
323.81729.57282.33850.61470.392227.2107-6.4633-7.151310.4490116.73700
475.136230.92053.16110.31680.1832-5.527728.552521.83934.6480356.46200
551.624021.15022.90850.24200.134050.876143.18879.81453.6130238.09800
648.246817.35653.03320.25290.15158.573038.095710.58684.7920219.08700
726.789713.75952.55210.42360.217429.633920.4560-2.92920.8120237.13400
896.232746.51654.15400.07790.0390110.355085.048643.18444.8540248.22600
946.761915.19932.57860.33770.191324.754843.8771-6.68127.8750102.25100

Last rows

FLengthFWidthFSizeFConcFConc1FAsymFM3LongFM3TransFAlphaFDisttarget
1901032.490210.67232.47420.46640.2735-27.0097-21.16878.481369.1730120.66801
1901179.552844.99293.54880.16560.0900-39.621353.7866-30.005415.8075311.56801
1901231.837313.87342.82510.41690.1988-16.4919-27.144811.109811.3663100.05661
19013182.500376.55683.68720.11230.0666192.267593.0302-62.619282.1691283.47311
1901443.298017.35452.83070.28770.1646-60.1842-33.8513-3.654578.4099224.82991
1901521.384610.91702.61610.58570.393415.261811.52452.87662.4229106.82581
1901628.94526.70202.26720.53510.278437.081613.1853-2.963286.7975247.45601
1901775.445547.53053.44830.14170.0549-9.356141.0562-9.466230.2987256.51661
19018120.513576.90183.99390.09440.06835.8043-93.5224-63.838984.6874408.31661
19019187.181453.00143.20930.28760.1539-167.3125-168.455831.475552.7310272.31741

Duplicate rows

Most frequent

FLengthFWidthFSizeFConcFConc1FAsymFM3LongFM3TransFAlphaFDisttargetcount
012.917611.35962.11230.74130.390015.0388-5.6768-11.563864.9330227.107012
112.980110.88152.41750.74570.4723-13.69706.0371-7.001930.803078.261812
213.028710.95442.20000.75710.4511-14.09855.7807-10.174864.8700182.980012
314.791211.79552.30750.67490.45571.35334.7675-9.061162.250062.524512
416.756611.30632.37660.58400.35500.00000.15436.741948.5040117.636012
516.989411.00022.45640.62940.3514-3.49028.0823-7.051655.393091.376112
618.434317.87172.38470.48660.2701-15.7044-16.5170-12.231171.0730158.703012
718.49149.76352.48290.65790.3734-1.80607.65206.726033.8161188.867012
818.809011.13052.54960.60370.42450.5645-3.060811.817675.5740222.591012
919.084813.73462.58490.60080.396617.282419.4696-5.23905.3161213.714012